A Comparative Analysis of Lexical Bundles Used by Native and Non-native Scholars
Bibliographic record
Abstract
<p>In the recent years, globalization prepared a ground for English to be the lingua franca of the academia. Thus, most highly prestigious international journals have defined their medium of publications as English. However, even advanced language learners have difficulties in writing their research articles due to the lack of appropriate lexical knowledge and discourse conventions of academia. Considering the fact that the underuse, overuse and misuse of formulaic sequences or lexical bundles are often characterized with non-native writers of English, lexical bundle studies have recently been on the top of the agenda of corpus studies. Although the related literature has represented specific genres or disciplines, no study has scrutinized lexical bundles in the research articles that are written in the educational sciences. Therefore, the current study compared the structural and functional characteristics of the lexical-bundle use in L1 and L2 research articles in English. The results revealed the deviation of the usages of lexical bundles by the non-native speakers of English from the native speaker norms. Furthermore, the results indicated the overuse of clausal or verb-phrase based lexical bundles in the research articles of Turkish scholars while their native counterparts used noun and prepositional phrase-based lexical bundles more than clausal bundles.</p>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".